• Title, Summary, Keyword: Random Forest: Classification: Recognition

Search Result 12, Processing Time 0.029 seconds

Human Action Recognition in Still Image Using Weighted Bag-of-Features and Ensemble Decision Trees (가중치 기반 Bag-of-Feature와 앙상블 결정 트리를 이용한 정지 영상에서의 인간 행동 인식)

  • Hong, June-Hyeok;Ko, Byoung-Chul;Nam, Jae-Yeal
    • The Journal of Korean Institute of Communications and Information Sciences
    • /
    • v.38A no.1
    • /
    • pp.1-9
    • /
    • 2013
  • This paper propose a human action recognition method that uses bag-of-features (BoF) based on CS-LBP (center-symmetric local binary pattern) and a spatial pyramid in addition to the random forest classifier. To construct the BoF, an image divided into dense regular grids and extract from each patch. A code word which is a visual vocabulary, is formed by k-means clustering of a random subset of patches. For enhanced action discrimination, local BoF histogram from three subdivided levels of a spatial pyramid is estimated, and a weighted BoF histogram is generated by concatenating the local histograms. For action classification, a random forest, which is an ensemble of decision trees, is built to model the distribution of each action class. The random forest combined with the weighted BoF histogram is successfully applied to Standford Action 40 including various human action images, and its classification performance is better than that of other methods. Furthermore, the proposed method allows action recognition to be performed in near real-time.

Machine Learning Based Domain Classification for Korean Dialog System (기계학습을 이용한 한국어 대화시스템 도메인 분류)

  • Jeong, Young-Seob
    • Journal of Convergence for Information Technology
    • /
    • v.9 no.8
    • /
    • pp.1-8
    • /
    • 2019
  • Dialog system is becoming a new dominant interaction way between human and computer. It allows people to be provided with various services through natural language. The dialog system has a common structure of a pipeline consisting of several modules (e.g., speech recognition, natural language understanding, and dialog management). In this paper, we tackle a task of domain classification for the natural language understanding module by employing machine learning models such as convolutional neural network and random forest. For our dataset of seven service domains, we showed that the random forest model achieved the best performance (F1 score 0.97). As a future work, we will keep finding a better approach for domain classification by investigating other machine learning models.

Design and Implementation of Indoor Location Recognition System based on Fingerprint and Random Forest (핑거프린트와 랜덤포레스트 기반 실내 위치 인식 시스템 설계와 구현)

  • Lee, Sunmin;Moon, Nammee
    • Journal of Broadcast Engineering
    • /
    • v.23 no.1
    • /
    • pp.154-161
    • /
    • 2018
  • As the number of smartphone users increases, research on indoor location recognition service is necessary. Access to indoor locations is predominantly WiFi, Bluetooth, etc., but in most quarters, WiFi is equipped with WiFi functionality, which uses WiFi features to provide WiFi functionality. The study uses the random forest algorithm, which employs the fingerprint index of the acquired WiFi and the use of the multI-value classification method, which employs the receiver signal strength of the acquired WiFi. As the data of the fingerprint, a total of 4 radio maps using the Mac address together with the received signal strength were used. The experiment was conducted in a limited indoor space and compared to an indoor location recognition system using an existing random forest, similar to the method proposed in this study for experimental analysis. Experiments have shown that the system's positioning accuracy as suggested by this study is approximately 5.8 % higher than that of a conventional indoor location recognition system using a random forest, and that its location recognition speed is consistent and faster than that of a study.

Finding a plan to improve recognition rate using classification analysis

  • Kim, SeungJae;Kim, SungHwan
    • International journal of advanced smart convergence
    • /
    • v.9 no.4
    • /
    • pp.184-191
    • /
    • 2020
  • With the emergence of the 4th Industrial Revolution, core technologies that will lead the 4th Industrial Revolution such as AI (artificial intelligence), big data, and Internet of Things (IOT) are also at the center of the topic of the general public. In particular, there is a growing trend of attempts to present future visions by discovering new models by using them for big data analysis based on data collected in a specific field, and inferring and predicting new values with the models. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable, the correlation between the variables, and multicollinearity. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified according to the purpose of analysis. Therefore, in this study, data is classified using a decision tree technique and a random forest technique among classification analysis, which is a machine learning technique that implements AI technology. And by evaluating the degree of classification of the data, we try to find a way to improve the classification and analysis rate of the data.

An Improved Approach for 3D Hand Pose Estimation Based on a Single Depth Image and Haar Random Forest

  • Kim, Wonggi;Chun, Junchul
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.9 no.8
    • /
    • pp.3136-3150
    • /
    • 2015
  • A vision-based 3D tracking of articulated human hand is one of the major issues in the applications of human computer interactions and understanding the control of robot hand. This paper presents an improved approach for tracking and recovering the 3D position and orientation of a human hand using the Kinect sensor. The basic idea of the proposed method is to solve an optimization problem that minimizes the discrepancy in 3D shape between an actual hand observed by Kinect and a hypothesized 3D hand model. Since each of the 3D hand pose has 23 degrees of freedom, the hand articulation tracking needs computational excessive burden in minimizing the 3D shape discrepancy between an observed hand and a 3D hand model. For this, we first created a 3D hand model which represents the hand with 17 different parts. Secondly, Random Forest classifier was trained on the synthetic depth images generated by animating the developed 3D hand model, which was then used for Haar-like feature-based classification rather than performing per-pixel classification. Classification results were used for estimating the joint positions for the hand skeleton. Through the experiment, we were able to prove that the proposed method showed improvement rates in hand part recognition and a performance of 20-30 fps. The results confirmed its practical use in classifying hand area and successfully tracked and recovered the 3D hand pose in a real time fashion.

Covariance-based Recognition Using Machine Learning Model

  • Osman, Hassab Elgawi
    • Proceedings of the Korean Society of Broadcast Engineers Conference
    • /
    • /
    • pp.223-228
    • /
    • 2009
  • We propose an on-line machine learning approach for object recognition, where new images are continuously added and the recognition decision is made without delay. Random forest (RF) classifier has been extensively used as a generative model for classification and regression applications. We extend this technique for the task of building incremental component-based detector. First we employ object descriptor model based on bag of covariance matrices, to represent an object region then run our on-line RF learner to select object descriptors and to learn an object classifier. Experiments of the object recognition are provided to verify the effectiveness of the proposed approach. Results demonstrate that the propose model yields in object recognition performance comparable to the benchmark standard RF, AdaBoost, and SVM classifiers.

  • PDF

Feature Selection for Classification of Mass Spectrometric Proteomic Data Using Random Forest (단백체 스펙트럼 데이터의 분류를 위한 랜덤 포리스트 기반 특성 선택 알고리즘)

  • Ohn, Syng-Yup;Chi, Seung-Do;Han, Mi-Young
    • Journal of the Korea Society for Simulation
    • /
    • v.22 no.4
    • /
    • pp.139-147
    • /
    • 2013
  • This paper proposes a novel method for feature selection for mass spectrometric proteomic data based on Random Forest. The method includes an effective preprocessing step to filter a large amount of redundant features with high correlation and applies a tournament strategy to get an optimal feature subset. Experiments on three public datasets, Ovarian 4-3-02, Ovarian 7-8-02 and Prostate shows that the new method achieves high performance comparing with widely used methods and balanced rate of specificity and sensitivity.

Analysis of facial expression recognition (표정 분류 연구)

  • Son, Nayeong;Cho, Hyunsun;Lee, Sohyun;Song, Jongwoo
    • The Korean Journal of Applied Statistics
    • /
    • v.31 no.5
    • /
    • pp.539-554
    • /
    • 2018
  • Effective interaction between user and device is considered an important ability of IoT devices. For some applications, it is necessary to recognize human facial expressions in real time and make accurate judgments in order to respond to situations correctly. Therefore, many researches on facial image analysis have been preceded in order to construct a more accurate and faster recognition system. In this study, we constructed an automatic recognition system for facial expressions through two steps - a facial recognition step and a classification step. We compared various models with different sets of data with pixel information, landmark coordinates, Euclidean distances among landmark points, and arctangent angles. We found a fast and efficient prediction model with only 30 principal components of face landmark information. We applied several prediction models, that included linear discriminant analysis (LDA), random forests, support vector machine (SVM), and bagging; consequently, an SVM model gives the best result. The LDA model gives the second best prediction accuracy but it can fit and predict data faster than SVM and other methods. Finally, we compared our method to Microsoft Azure Emotion API and Convolution Neural Network (CNN). Our method gives a very competitive result.

지능형 IoT서비스를 위한 기계학습 기반 동작 인식 기술

  • Choe, Dae-Ung;Jo, Hyeon-Jung
    • The Proceeding of the Korean Institute of Electromagnetic Engineering and Science
    • /
    • v.27 no.4
    • /
    • pp.19-28
    • /
    • 2016
  • 최근 RFID와 같은 무선 센싱 네트워크 기술과 객체 추적을 위한 센싱 디바이스 및 다양한 컴퓨팅 자원들이 빠르게 발전함에 따라, 기존 웹의 형태는 소셜 웹에서 유비쿼터스 컴퓨팅 웹으로 자연스럽게 진화되고 있다. 유비쿼터스 컴퓨팅 웹에서 사물인터넷(IoT)은 기존의 컴퓨터를 대체할 수 있는데, 이것은 곧 한 사람과 주변 사물들 간에 연결되는 네트워크가 확장되는 것과 동시에 네트워크 안에서 생성되는 데이터의 수가 기하급수적으로 증가되는 것을 의미한다. 따라서 보다 지능적인 IoT 서비스를 위해서는, 수많은 미가공 데이터들 사이에서 사람의 의도와 상황을 실시간으로 정확히 파악할 수 있어야 한다. 이때 사물과의 상호작용을 위한 동작 인식 기술(Gesture recognition)은 집적적인 접촉을 필요로 하지 않기 때문에, 미래의 사람-사물 간 상호작용에 응용될 수 있는 잠재력을 갖고 있다. 한편, 기계학습 분야의 최신 알고리즘들은 다양한 문제에서 사람의 인지능력을 종종 뛰어넘는 성능을 보이고 있는데, 그 중에서도 의사결정나무(Decision Tree)를 기반으로 한 Decision Forest는 분류(Classification)와 회귀(Regression)를 포함한 전 영역에 걸쳐 우월한 성능을 보이고 있다. 따라서 본 논문에서는 지능형 IoT 서비스를 위한 다양한 동작 인식 기술들을 알아보고, 동작 인식을 위한 Decision Forest의 기본 개념과 구현을 위한 학습, 테스팅에 대해 구체적으로 소개한다. 특히 대표적으로 사용되는 3가지 학습방법인 배깅(Bagging), 부스팅(Boosting) 그리고 Random Forest에 대해 소개하고, 이것들이 동작 인식을 위해 어떠한 특징을 갖는지 기존의 연구결과를 토대로 알아보았다.

Vehicle Headlight and Taillight Recognition in Nighttime using Low-Exposure Camera and Wavelet-based Random Forest (저노출 카메라와 웨이블릿 기반 랜덤 포레스트를 이용한 야간 자동차 전조등 및 후미등 인식)

  • Heo, Duyoung;Kim, Sang Jun;Kwak, Choong Sub;Nam, Jae-Yeal;Ko, Byoung Chul
    • Journal of Broadcast Engineering
    • /
    • v.22 no.3
    • /
    • pp.282-294
    • /
    • 2017
  • In this paper, we propose a novel intelligent headlight control (IHC) system which is durable to various road lights and camera movement caused by vehicle driving. For detecting candidate light blobs, the region of interest (ROI) is decided as front ROI (FROI) and back ROI (BROI) by considering the camera geometry based on perspective range estimation model. Then, light blobs such as headlights, taillights of vehicles, reflection light as well as the surrounding road lighting are segmented using two different adaptive thresholding. From the number of segmented blobs, taillights are first detected using the redness checking and random forest classifier based on Haar-like feature. For the headlight and taillight classification, we use the random forest instead of popular support vector machine or convolutional neural networks for supporting fast learning and testing in real-life applications. Pairing is performed by using the predefined geometric rules, such as vertical coordinate similarity and association check between blobs. The proposed algorithm was successfully applied to various driving sequences in night-time, and the results show that the performance of the proposed algorithms is better than that of recent related works.